Senior Director of Software Engineering

Your opportunity to make a real impact and shape the future of financial services is waiting for you.

Let's push the boundaries of what's possible together.

As a Senior Director of Software Engineering at JPMorganChase within Corporate Technology, you lead the strategy, engineering, and end-to-end delivery of the department's Databricks-based data lake and consumption platform on AWS.

You provide direction to global engineering teams building data pipelines, APIs, reporting products, and self-service analytics capabilities.

Partnering with business, product, technology, and control stakeholders, you translate business priorities into a platform roadmap and deliver secure, governed, scalable capabilities that support production use.

Your leadership connects platform architecture, engineering execution, and stakeholder adoption.

Job responsibilities


* Own the platform strategy, roadmap, and delivery across data onboarding, access provisioning, and consumption capabilities, balancing business demand, engineering dependencies, and operational risk.


* Lead and develop global, distributed engineering teams delivering data pipelines, APIs, front-end data products, and self-service analytics; establish technical standards, delivery accountability, and cross-team collaboration.


* Direct data onboarding and integration using Unity Catalog, Delta Sharing, and Lakehouse Federation, enabling governed access across workspaces, catalogs, and lines of business.


* Partner with senior business, product, technology, audit, and compliance stakeholders to prioritize investments, resolve competing objectives, and communicate architectural choices and delivery trade-offs.


* Establish and enforce onboarding controls for data classification, metadata, lineage, and access, ensuring source-data classification approval and clear ownership before data is made available for consumption.


* Own platform production readiness, scalability, resilience, and audit readiness; drive remediation of control gaps involving service identities, credential lifecycle, classification coverage, and schema changes.


* Define and monitor service-level objectives for stakeholder-facing data products, with clear accountability for reliability, support, and operational performance.
Evaluate and adopt Databricks-native capabilities, including declarative pipelines and conversational analytics, where they improve usability, governance, delivery efficiency, or cost.


* Set standards for enterprise-authorized AI-assisted and agentic engineering workflows across teams, including automated testing and release governance, with appropriate human oversight, security, validation, resilience, and traceability.


* Sets and scales multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation (using enterprise-authorized tools within the work environment) to drive firmwide objectives (speed, scalability, reliability, and cost-to-serve),...